partition

name	partition
description	Split the attack surface into parallel-discovery partitions so fan-out hunters explore different subsystems instead of converging on the same shallow bug. Deterministic grouping of /x-ray entry points by component → .kuzushi/partitions.json, which a hunt coordinator hands to one subagent per partition. Run /x-ray first.
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Partition

Discovery parallelizes well, but naive fan-out makes agents converge on the same shallow bugs. The fix (from Anthropic's defending-code harness) is a first pass that partitions the search space by subsystem, then gives each partition its own discovery agent. /partition does that grouping deterministically so parallel hunters cover different components.

Run it

node "${CLAUDE_PLUGIN_ROOT}/scripts/cmd/partition.mjs" --target "<repo root>" — reads the entry points /x-ray mapped (.kuzushi/x-ray/entry-points.json), groups them by subsystem into non-overlapping partitions (cap 6 by default; the long tail merges into other), and writes .kuzushi/partitions.json. If it errors "run /x-ray first", do that — the partition is only as good as the mapped attack surface.

Fan out the hunt

Each partition has { id, label, focusHint, attackSurface: [entry points] }. A coordinator (/threat-hunt, /taint-analysis) spawns one hunter subagent per partition in parallel, each scoped to its partition's attackSurface and told (via focusHint) to stay in that subsystem. This both speeds discovery and raises recall — agents aren't racing to the same easy finding, so the long tail of per-subsystem bugs gets attention. Promote findings into the shared index as usual; the fingerprint dedupes anything two partitions both surface at a boundary.

When NOT to use

• Before /x-ray — there's no mapped attack surface to split; partitioning an empty surface does nothing. Map first.
• On a tiny target (a handful of entry points in one component) — one partition is fine; fan-out overhead buys nothing.
• As a finder — /partition produces no findings; it only scopes where the hunters look.

Rationalizations to Reject

• "More parallel agents = more bugs, just scale it." → Without partitioning they converge on the same shallow findings; the partition is what makes parallelism pay off. Scale across partitions, not redundantly within one.
• "One giant component holds everything, so one partition is fine." → If a component dominates, that's a signal to split it finer (by boundary kind or sub-path), not to hunt it as one blob — the long tail hides there. Re-run with a higher maxPartitions or sub-scope by hand.
• "Partitions overlap, so I'll dedupe later." → They're non-overlapping by construction (each entry point lands in one partition); if you hand a subagent more than its attackSurface, you've reintroduced the convergence the partition exists to prevent.